Sr.Security ML / AI Engineer

Overview

Who we are

Toyota does not offer support or sponsorship of job applicants for employment-based visas or any other work authorization for this role now or in the future. You must have the right to work in the United States and not require Toyota support or sponsorship for immigration-related employment (e.g., H-1B, O-1, E-3, H-1B1, TN, F-1 OPT, F-1 STEM OPT, F-1 CPT, ‘job flexibility benefits’ [also known as I-140 or Adjustment of Status portability], etc.) now or in the future. You should not apply for this role if you will require Toyota to assist with immigration support or sponsorship now or in the future.

Who We're Looking For

Toyota Financial Services (TFS) Technology team is looking for a highly motivated person to fill a role as an Sr. ML/AI Security Engineer within the Security Intelligence Engineering organization. You'll own the intelligence layer of a new AI-powered security platform — starting with prompt engineering and managed AI service integration, then progressing to fine-tuning models on enterprise security data, and building a multi-model serving and routing layer. This role is what makes the organization own its intelligence rather than renting it from a vendor. You'll train models that understand the specific security environment, build the feedback loops that make them better over time, and ensure the AI layer delivers high accuracy on alert triage while keeping costs predictable through intelligent model routing.

What you'll be doing

• Design and implement prompt engineering patterns for managed AI service integration

• Build training data pipelines from the security data lake — curating, labeling, and versioning datasets from real enterprise security telemetry

• Fine-tune models on organization-specific security data — alert triage, risk scoring, finding classification

• Implement the analyst feedback loop — capturing human corrections to continuously improve model accuracy

• Build model evaluation frameworks with rigorous metrics (F1, precision, recall, false positive rates) benchmarked against analyst agreement

• Design and implement a model routing layer — directing each task to the optimal model based on complexity, latency requirements, and cost

• Monitor models in production for drift, accuracy degradation, and emerging failure modes

• Implement centralized token usage monitoring for leadership visibility into AI consumption and cost control

• Collaborate with the Lead Engineer on agent architectures — multi-agent orchestration, tool use, and autonomous triage workflows

• Deploy and manage model inference endpoints across cloud ML services and container-based serving

• Build the analyst feedback loop: approval/rejection signals in dashboards feeding back into retraining pipelines

What You Bring

• 3+ years in applied ML/AI engineering (not research-only — production deployment required)

• Hands-on experience with LLM fine-tuning — LoRA, QLoRA, or full fine-tuning on domain-specific data

• Experience with cloud ML platforms (e.g., AWS SageMaker): training jobs, hyperparameter tuning, model registry, endpoint deployment

• PyTorch proficiency for model training and custom architectures

• Experience building evaluation pipelines — automated metrics, human evaluation protocols, A/B testing

• Understanding of transformer architectures and attention mechanisms (not just API calls)

• Python fluency with production engineering practices (testing, CI/CD, monitoring)

• Strong communication skills with the ability to explain model behavior and limitations to non-ML stakeholders

Added bonus if you have

• Experience with security or cybersecurity data — alert classification, threat detection, anomaly detection